Prior art emotion modeling technology includes rule-based discrete emotion modeling and vector space-based continuous emotion modeling.
A representative example of discrete emotion modeling is an OCC (Ortony, Collins, and Clore)-based emotion model (Em), which was designed for the development of an emotional agent in the Oz Project, undertaken at Carnegie Mellon University. In the OCC model, similar emotions are defined as a single emotion type, and the cause due to which an emotion type occurs is also described in each defined emotion type. Furthermore, emotion types, which occur based on combinations of other emotion types, are defined. Em is designed to determine causes due to which emotions, defined based on the OCC model, occur and to create emotions having the intensities of occurrence based on the determined causes. Although the discrete emotion modeling method of creating relevant emotions according to predefined causes (conditions) has an advantage in that it is easy to implement and evaluate an emotion model, it has a disadvantage in that it is difficult to create emotions in unexpected situations because emotions can be represented only when the conditions are satisfied.
In continuous emotion modeling, the dimensions of a vector space are defined in consideration of stimuli affecting emotions, and an emotion vector space, that is, an emotion model, is created by dividing the defined vector space into emotion regions. In the emotion vector space, an arbitrary vector refers to an emotional state, and a current emotional state is calculated from an immediately previous emotional state and a currently detected stimulus. As a representative example, there is the Mental Space model, which is the three-dimensional emotion vector space used in WE-4R, which is a social robot that was developed at Waseda University. The continuous emotion modeling method has an advantage in that it can represent emotions even in unexpected situations.
Although the prior art continuous emotion modeling method has the above advantage, as described above, there are disadvantages in that it is not easy to divide a multidimensional emotion vector space into emotion regions, and, particularly, it is difficult to perform the representation of a composite emotion that simultaneously represents one or more emotions having the intensity of occurrence. There is a method of performing fuzzy division on a vector space using a fuzzy membership function in order to represent a composite emotion. However, this method has problems in that it is difficult to determine the shape of a fuzzy membership function and in that the efficiency related to the calculation time is decreased as the number of dimensions of an vector space increases. There is another method of creating a continuous emotion model using a neural network. However, this method has a problem in that it is not easy to construct learning data in consideration of all situations in order to teach a neural network model.